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Is product life cycle theory a special case? Dominant designs and the emergence of market niches through coevolutionary-learning

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Structural Change and Economic Dynamics 1998年第1期9卷 109-134页

作者： Windrum, Paul Birchenhall, Chris PREST University of Manchester Manchester M13 9PL Oxford Road United Kingdom School of Economic Studies University of Manchester Manchester M13 9PL Dover Street United Kingdom

Product life cycle theory has been a key organizing principle in studies of technical innovation over the last 20 years and is promoted by leading management theorists as a tool for strategic decision making. This paper critically appraises the dominant design concept that lies at the heart of this theory. In so doing it seriously questions a basic premise adopted by numerous economists;that the dynamics of technological change can be understood through the examination of the technological artefact. We put forward an alternative explanation for the patterns of product innovation observed in infant industries. This alternative views technological innovation as a coupled, second-order learning system comprising a population of consumers and a population of firms. Within this approach the artefact is viewed in a quite different light. Rather than being an object in itself with its own internal drives and dynamics, it is a mediating device. The form of the mediating device alters over time due to changes in the external factors acting upon it;that is to say, its form changes as a consequence of the formation and development of user preferences through consumer learning and of the technological experimentation and beaming of firms. Turning specifically to the dominant design concept, we argue that this is but one possible outcome of the innovation system. It is shown that, while the system tends to converge towards a limited number of design configurations, there is no reason to expect it to stabilize around a single design. It may also stabilize through a process of market differentiation leading to the emergence of distinct niches. Observations drawn from the markets for cameras, road vehicles, amplification systems and personal computers support the contention that the dominant design outcome is a 'special case' - a case of convergence to a single market niche. The theoretical and empirical analysis is formalized through a simulation model of the innovation system. Th

关键词： Innovation product life cycle learning algorithm Market niches Population beaming Technological

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Morphological perceptron learning

Morphological perceptron learning

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Joint Conference on the Science and Technology of Intelligent Systems ISIC/CIRA/ISAS

作者： Sussner, P Univ Estadual Campinas Inst Math Stat & Sci Computat Dept Appl Math BR-13081970 Campinas SP Brazil

ISBN: (纸本)0780344235

During the last decade, researchers have applied neural networks to a multitude of difficult tasks which would normally require human intelligence. In particular, perceptrons are used to classify patterns into different classes. Recently, several researchers introduced a novel class of artificial neural networks, called morphological neural networks. In this new theory, the first step in computing the next state of a neuron of in performing the next layer neural network computation involves the nonlinear operation of adding neural values and their synaptic strengths followed by forming the maximum of the results. We have shown in previous papers that the properties of morphological neural networks differ drastically from those of traditional neural network models. In this paper, we introduce a learning algorithm for multilayer morphological;perceptrons which is capable of solving arbitrary classification problems of patterns into two classes.

关键词： perceptrons morphological neural networks learning algorithm classification of patterns

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Enabling Intelligence at Network Edge: An Overview of Federated learning

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ZTE Communications 2020年第2期18卷 2-10页

作者： Howard H.YANG ZHAO Zhongyuan Tony Q.S.QUEK Singapore University of Technology and Design Singapore 487372Singapore Beijing University of Post and Telecommunication Beijing 100876China

The burgeoning advances in machine learning and wireless technologies are forg?ing a new paradigm for future networks, which are expected to possess higher degrees of in?telligence via the inference from vast dataset and being able to respond to local events in a timely manner. Due to the sheer volume of data generated by end-user devices, as well as the increasing concerns about sharing private information, a new branch of machine learn?ing models, namely federated learning, has emerged from the intersection of artificial intelli?gence and edge computing. In contrast to conventional machine learning methods, federated learning brings the models directly to the device for training, where only the resultant param?eters shall be sent to the edge servers. The local copies of the model on the devices bring along great advantages of eliminating network latency and preserving data privacy. Never?theless, to make federated learning possible, one needs to tackle new challenges that require a fundamental departure from standard methods designed for distributed optimizations. In this paper, we aim to deliver a comprehensive introduction of federated learning. Specifical?ly, we first survey the basis of federated learning, including its learning structure and the distinct features from conventional machine learning models. We then enumerate several critical issues associated with the deployment of federated learning in a wireless network, and show why and how technologies should be jointly integrated to facilitate the full imple?mentation from different perspectives, ranging from algorithmic design, on-device training, to communication resource management. Finally, we conclude by shedding light on some po?tential applications and future trends.

关键词： federated learning edge intelligence learning algorithm communication efficiency privacy and security

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learning approach to fault tolerant control: An overview

Learning approach to fault tolerant control: An overview

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Joint Conference on the Science and Technology of Intelligent Systems ISIC/CIRA/ISAS

作者： Polycarpou, MM Vemuri, AT Univ Cincinnati Dept Elect & Comp Engn & Comp Sci ECE CS Cincinnati OH 45221 USA

ISBN: (纸本)0780344235

This paper presents an overview of a learning methodology for detecting, identifying and accommodating faults in nonlinear dynamic systems. The main idea behind this approach is to monitor the plant for any off-nominal behavior due to faults utilizing a neural network or other types of online approximators. In the presence of a failure, the neural network can be used as an estimate of the nonlinear fault function for fault identification and accommodation purposes. Furthermore, during the initial stage of monitoring, the learning capabilities of the neural network can be used to learn the modeling errors, thereby enhancing the robustness properties of the fault diagnosis scheme.

关键词： nonlinear fault diagnosis learning algorithm fault accommodation on-line approximation

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Automatic Exploration Process Adjustment for Safe Reinforcement learning with Joint Chance Constraint Satisfaction 21st

Automatic Exploration Process Adjustment for Safe Reinforcem...

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Okawa, Yoshihiro Sasaki, Tomotake Iwane, Hidenao Fujitsu Labs Ltd Artificial Intelligence Lab Nakahara Ku 4-1-1 Kamikodanaka Kawasaki Kanagawa 2118588 Japan Reading Skill Test Inc Res & IT Div Chuo Ku 2-1-4 Shinkawa Tokyo 1040033 Japan Fujitsu Labs Ltd Kawasaki Kanagawa Japan

In reinforcement learning (RL) algorithms, exploratory control inputs are used during learning to acquire knowledge for decision making and control, while the true dynamics of a controlled object is unknown. However, this exploring property sometimes causes undesired situations by violating constraints regarding the state of the controlled object. In this paper, we propose an automatic exploration process adjustment method for safe RL in continuous state and action spaces utilizing a linear nominal model of the controlled object. Specifically, our proposed method automatically selects whether the exploratory input is used or not at each time depending on the state and its predicted value as well as adjusts the variance-covariance matrix used in the Gaussian policy for exploration. We also show that our exploration process adjustment method theoretically guarantees the satisfaction of the constraints with the pre-specified probability, that is, the satisfaction of a joint chance constraint at every time. Finally, we illustrate the validity and the effectiveness of our method through numerical simulation. Copyright (C) 2020 The Authors.

关键词： Reinforcement learning learning algorithm Safe exploration Safety-critical Chance constraint

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Tracking Control of Piezoelectric Actuators using Feedforward/Feedback learning-based Controller 6

Tracking Control of Piezoelectric Actuators using Feedforwar...

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6th International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Saleem, Ashraf Al Hatton, Musabah Shafiq, Mohammed Bahadur, Issam Sultan Qaboos Univ Elect & Comp Engn Dept Muscat Oman Sultan Qaboos Univ Mech & Ind Engn Dept Muscat Oman

ISBN: (纸本)9781728105215

Piezoelectric actuators are commonly used in Micro/nanopositioning due to their high stiffness, fast response and ultra-high precision. However, three main factors affect their position tracking performance, namely hysteresis, creep, and structural vibrations. To overcome these limitations, a feedforward/feedback learning-based controller is proposed in this paper. The proposed controller is a learning-based controller that is capable of smoothly tracking continuous sinusoid reference signals. Simulation and experimental results using a commercial amplified piezoelectric actuator attest to the good tracking performance the proposed controller.

关键词： Piezoelectric Actuators feedforward/feedback controller learning algorithm position tracking control

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A Neural Architecture to Address Reinforcement learning Problems

A Neural Architecture to Address Reinforcement Learning Prob...

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International Joint Conference on Neural Networks (IJCNN)

作者： de Arruda, Rodrigo L. S. Von Zuben, Fernando J. Univ Campinas UNICAMP Sch Elect & Comp Engn FEEC Dept Comp Engn & Ind Automat DCA Lab Bioinformat & Bioinspired Comp LBiC Campinas SP Brazil

ISBN: (纸本)9781424496365

In this paper, the Reinforcement learning problem is formulated equivalently to a Markov Decision Process. We address the solution of such problem using a novel Adaptive Dynamic Programming algorithm which is based on a Multi-layer Perceptron Neural Network composed of a parameterized function approximator called Wire-Fitting. Extending such established model, this work makes use of concepts of eligibility to conceive faster learning algorithms. The advantage of the proposed approach is founded on the capability to handle continuous environments and to learn a better policy while following another. Simulation results involving the automatic control of an inverted pendulum are presented to indicate the effectiveness of the proposed algorithm.

关键词： Approximation methods Dynamic programming Equations Heuristic algorithms Markov decision process Markov processes Markov processes Mathematical model Monte Carlo methods adaptive dynamic programming automatic control dynamic programming inverted pendulum learning (artificial intelligence) learning algorithm multilayer perceptron neural network multilayer perceptrons neural architecture neural net architecture parameterized function approximator reinforcement learning wire-fitting

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learning-Based Calibration Decision System for Bio-Inertial Motion Application 18

Learning-Based Calibration Decision System for Bio-Inertial ...

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18th IEEE Sensors Conference

作者： Askari, Sina Jao, Chi-Shih Wang, Yusheng Shkel, Andrei M. Univ Calif Irvine Dept Biomed Engn MicroSyst Lab Irvine CA 92697 USA Univ Calif Irvine Dept Mech & Aerosp Engn MicroSyst Lab Irvine CA 92697 USA

ISBN: (纸本)9781728116341

We developed a learning-based calibration algorithm for a vestibular prosthesis with the long-term goal of reproducing error-free vestibular system dynamic responses. Our approach uses an additional IMU to detect the head acceleration of a patient and to correct the corresponding drift in the vestibular prosthesis. The algorithm includes four major parts. First, we extract features from the shoe-mounted IMU to classify human activities through convolutional neural networks. Second, we fuse data from the head-mounted IMU (vestibular prosthesis). Third, we artificially create additional data samples from a small pool of training data for each classification class. Fourth, we use the classified activities to calibrate the reading from the head-mounted IMU. The results indicate that during daily routine activities the firing rate baseline of a vestibular prosthesis system without calibration fluctuates between 100 pulses/s to 150 pulses/s;in contrast, an appropriate calibration to human activity results in correction of 4 pulses/s in extreme cases, providing a stable baseline firing rate while the head is not moving. In this work, we specifically study the contribution of gyroscope scale factor on the drift of the vestibular prosthesis system and propose a corresponding calibration method.

关键词： Vestibular prosthesis multi-sensor fusion learning algorithm

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Temporal Difference learning with Interpolated N-Tuples: Initial Results from a Simulated Car Racing Environment

Temporal Difference Learning with Interpolated N-Tuples: Ini...

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IEEE Conference on Computational Intelligence and Games (CIG)

作者： Abdullahi, Aisha A. Lucas, Simon M. Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England

ISBN: (纸本)9781457700095

Evolutionary algorithms have been used successfully in car racing game competitions, such as the ones based on TORCS. This is in contrast to temporal difference learning (TDL), which despite being a powerful learning algorithm, has not been used to any significant extent within these competitions. We believe that this is mainly due to the difficulty of choosing a good function approximator, the potential instability of the learning behavior (and hence the reliability of the results), and the lack of a forward model which restricts the choice of TDL algorithms. This paper reports our initial results on using a new type of function approximator designed to be used with TDL for problems with a large number of continuous-valued inputs, where function approximators such as multi-layer perceptrons can be unstable. The approach combines interpolated tables with n-tuple systems. In order to conduct the research in a flexible and efficient way we developed a new car-racing simulator that runs much more quickly than TORCS and gives us full access to the forward model of the system. We investigate different types of tracks and physics models, and also make comparisons with human drivers and some initial tests with evolutionary learning (EL). The results show that each approach leads to different driving styles, and either TDL or EL can learn best depending on the details of the environment. Significantly, TDL produced best results when learning state-action values (similar to Q-learning;no forward model needed). Regarding driving style, TDL consistently learned behaviours that avoid damage while EL tended to evolve fast but reckless drivers.

关键词： Computational intelligence Computational modeling Function approximation Games Sensors Software TORCS car racing game competition car racing simulator continuous-valued inputs evolutionary algorithm evolutionary computation evolutionary learning forward model function approximator interpolated n-tuples learning (artificial intelligence) learning algorithm learning behavior multilayer perceptron reckless drivers simulated car racing environment state-action values temporal difference learning traffic engineering computing

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learning task applied to identification of a marine vehicle

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Annual Review in Automatic Programming 1994年第C期19卷 197-201页

作者： Garcia, R.Ferreiro Paz, J.Vidal Dep. Electrónica y Sisiemas Universidad de La Coruña 15011 La Coruña E.S. Marina Civil C/Paseo Ronda 51 Spain

This paper describes a learning algorithm to be applied in process control techniques, covering several topics of control applications such as system identification, or process supervision in a simple and useful way which make the method reliable to be applied on industrial process control. Knowledge acquired by means of a proposed learning algorithm is stored into a DAM or FAM (deterministic or fuzzy associative memory) for finally be applied on model parameter mapping. With such mappings, it is possible to know if the process is being disturbed and the intensity of such excitation inputs when no variation exist in system parameters or it is also possible to know the variation of system parameters when all input forces (control force and disturbances) are known. © 1995.

关键词： Deterministic associative memory learning algorithm parameter identification, rule-base updating, rule-base restriction

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